Dynamic Routing for Integrated Satellite-Terrestrial Networks: A Constrained Multi-Agent Reinforcement Learning Approach

The integrated satellite-terrestrial network (ISTN) system has experienced significant growth, offering seamless communication services in remote areas with limited terrestrial infrastructure. However, designing a routing scheme for ISTN is exceedingly difficult, primarily due to the heightened comp...

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Vydáno v:IEEE Journal on Selected Areas in Communications Ročník 42; číslo 5; s. 1204 - 1218
Hlavní autoři: Lyu, Yifeng, Hu, Han, Fan, Rongfei, Liu, Zhi, An, Jianping, Mao, Shiwen
Médium: Journal Article
Jazyk:angličtina
japonština
Vydáno: New York IEEE 01.05.2024
Institute of Electrical and Electronics Engineers (IEEE)
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Ablation
Algorithms
Computer Science - Artificial Intelligence
Computer Science - Machine Learning
Computer Science - Networking and Internet Architecture
constrained multi-agent reinforcement learning
Constraints
Delays
dynamic routing algorithm
Electrical Engineering and Systems Science - Systems and Control
end-to-end delay
Energy consumption
Ground stations
Heuristic algorithms
Integrated satellite-terrestrial networks
Lagrange multiplier
Machine learning
Multiagent systems
Optimization
Packet loss
Packet transmission
Routing
Satellites
Topology
ISSN:0733-8716, 1558-0008
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Popis
Shrnutí:The integrated satellite-terrestrial network (ISTN) system has experienced significant growth, offering seamless communication services in remote areas with limited terrestrial infrastructure. However, designing a routing scheme for ISTN is exceedingly difficult, primarily due to the heightened complexity resulting from the inclusion of additional ground stations, along with the requirement to satisfy various constraints related to satellite service quality. To address these challenges, we study packet routing with ground stations and satellites working jointly to transmit packets, while prioritizing fast communication and meeting energy efficiency and packet loss requirements. Specifically, we formulate the problem of packet routing with constraints as a max-min problem using the Lagrange method. Then we propose a novel constrained Multi-Agent reinforcement learning (MARL) dynamic routing algorithm named CMADR, which efficiently balances objective improvement and constraint satisfaction during the updating of policy and Lagrange multipliers. Finally, we conduct extensive experiments and an ablation study using the OneWeb and Telesat mega-constellations. Results demonstrate that CMADR reduces the packet delay by a minimum of 21% and 15%, while meeting stringent energy consumption and packet loss rate constraints, outperforming several baseline algorithms.
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ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2024.3365869